Long-range temperature forecasting

ABSTRACT

In an approach, a computer receives an observation dataset that identifies one or more ground truth values of an environmental variable at one or more times and a reforecast dataset that identifies one or more predicted values of the environmental variable produced by a forecast model that correspond to the one or more times. The computer then trains a climatology on the observation dataset to generate an observed climatology and trains the climatology on the reforecast dataset to generate a forecast climatology. The computer identifies observed anomalies by subtracting the observed climatology from the observation dataset and forecast anomalies by subtracting the forecast climatology from the reforecast dataset. The computer then models the observed anomalies as a function of the forecast anomalies, resulting in a calibration function, which the computer can then use to calibrate new forecasts received from the forecast model.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as aContinuation of application Ser. No. 16/240,610, filed Jan. 4, 2019,which is a Continuation of application Ser. No. 15/066,958, filed Mar.10, 2016, now U.S. Pat. No. 10,175,387, issued on Jan. 8, 2019, theentire contents of which is hereby incorporated by reference for allpurposes as if fully set forth herein. The applicants hereby rescind anydisclaimer of claim scope in the parent applications or the prosecutionhistory thereof and advise the USPTO that the claims in this applicationmay be broader than any claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to computer-based systems that areprogrammed for generating a long-range temperature forecast. Morespecifically, the present disclosure relates to using computer programsto generate a long-range temperature forecast or electronic digital dataprocessing apparatus for generating a long-range temperature forecast.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Weather forecasting is the application of science, technology, andstatistics to predict the state of the atmosphere for a given locationat some future point in time. The endeavor to fully understand Earth'sclimate system and to predict the weather has been a goal of humanityfor millennia. Weather forecasts are typically made by collectingquantitative data about the current state of the atmosphere at a givenplace and using the data to drive a simulation or physical model of theatmosphere to predict how the atmosphere will change over a given periodof time. For example, identifying changes in environmental variablessuch as temperature, air currents, barometric pressure, moisture, and soforth.

The collection of the quantitative data is performed by using varioustools, such as satellite image data, weather stations, temperaturereadings, humidity detectors, and so forth. Physical models used forweather forecasting decompose the earth (or other geographical region)into a uniform grid where the various environmental values are given aparticular value at each location within the grid. The physical modelthen runs through the grid simulating the physical processes that causechanges in weather over time to reach a future predicted state. However,the base data collected from the various weather stations do not fullycover the grid, nor are the readings taken at uniform times or withtools that have identical measurement errors. In fact, in most casesthere are far more points on the grid where the environmental variablesare unknown than known. As a result, to convert the base observationsinto values for each point on the grid, a process known as dataassimilation is performed which uses a combination of information fromthe gridded physical model with point observations to fill in the pointswhere observations are not explicitly available. The result is a valuefor each of the environmental variables for each point in the grid,which is collectively referred to as an “analysis”. The analysis is thenused to set the initial state of the physical model simulation which isstepped forward in time to predict the weather at some future time. Insome cases, a forecast model is also used to fill in the informationalgaps, which is referred to as an analysis/forecast cycle. In essence, aninitial condition is set by an analysis, a forecast is run from theanalysis, and the forecast is then used to fill in or smooth out thegaps in the next analysis in a repeating cycle.

Since the initial condition of the atmosphere generated by the analysisis uncertain due to the observational data being incomplete, climatescientists will often run forecasts using a set of different initialstates based on the known or estimated error of an analysis. Theresulting forecasts, each representing the future state of theatmosphere assuming that the values of the environmental variables inthe grid were in a slightly different initial state is referred to as aforecast ensemble. The overall behavior of the ensemble, rather thansimply one forecast, is then used to better capture the uncertainty inthe forecast.

In most cases, the analyses are performed by government agencies (and insome cases private agencies) and made freely available via variousdatabases, for example the U.S. Climate Forecast System (CFS), theEuropean Centre for Medium-Range Weather Forecasts (ECMWF), and soforth, provide open databases of analyses that can be accessed andanalyzed by weather scientists. These organizations often provideanalyses at different granularities of time, for instance six hours,daily, weekly, and so forth, as well as at different geographicalgranularities (for example different grid sizes).

Data assimilation techniques and forecasting models constantly evolveover time as atmospheric scientists develop a better understanding ofthe physical processes governing atmospheric evolution. As a result, ifone were to view the analyses taken by various public and privateorganizations over an extended period of time (for example the lastthirty-forty years), the changes in the data assimilation technique orforecasting model used can have a drastic impact on the analysis and theresulting forecast. To combat the non-uniformity of the techniques usedto create the original analysis, climate monitoring organizations willoften go back to the original observation data collected over a pastperiod of time and apply a consistent data assimilation technique(usually one that is more up-to-date than the original technique) fromthat past period of time to the present. As a result, theinconsistencies are removed and the skill of forecasting models can bemore easily evaluated. An analysis that is produced in this manner isreferred to as a “reanalysis” since the data is being reanalyzed using aconsistent technique.

Evaluating the skill of a forecast model requires a significant amountof forecasted predictions and corresponding observations with which tocompare those predictions. However, when testing a new model, it isimpractical to train on historical observation data and then evaluate atsome point in the future based upon the analysis generated at that time.Especially for longer range forecasts, it might take over a month beforea given forecast can be evaluated, and years or decades before enoughdata can be collected to tell whether the forecast model is actuallyskillful. As a result, climate scientists often perform “reforecasts”,which is a forecast based on past analyses (or more preferablyreanalyses). For example, if a reanalysis covers the past 30 years, aforecast model can be initialized from those conditions and used toproduce simulated forecasts across the 30 year period. Thus, areforecast provides evidence of what a forecast model would predict ifit had been used to forecast environmental conditions at some previouspoint in time. As a result, the predictive skill of the forecast modelcan be evaluated at a variety of leads by comparing the predictions tothe corresponding observed environmental conditions at that time.

SUMMARY OF THE DISCLOSURE

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more externaldata sources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 illustrates a structural overview of a forecast calibrationsystem according to an embodiment.

FIG. 6 illustrates a functional overview of a forecast calibrationsystem according to an embodiment.

FIG. 7 illustrates a process for generating a calibration function for aforecast model according to an embodiment.

FIG. 8 illustrates a process for calibrating a new forecast according toan embodiment.

FIG. 9 depicts an example embodiment of a timeline view for data entry.

FIG. 10 depicts an example embodiment of a spreadsheet view for dataentry.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. The description is provided according to the followingoutline:

-   -   1.0 General Overview    -   2.0 Example Agricultural Intelligence Computer System        -   2.1 Structural Overview        -   2.2 Application Program Overview        -   2.3 Data Ingest to the Computer System        -   2.4 Process Overview—Agronomic Model Training        -   2.5 Forecast Calibration System            -   2.5.1 Forecast Calibration System Structural Overview            -   2.5.2 Forecast Calibration System Functional Overview        -   2.6 Implementation Example—Hardware Overview    -   3.0 Example System Inputs    -   4.0 Climatology    -   5.0 Nonhomogeneous Gaussian Regression    -   6.0 Modified Nonhomogeneous Gaussian Regression    -   7.0 Covariants    -   8.0 Model Variants    -   9.0 Calibration Training Process Flow    -   10.0 Forecast Calibration Process Flow    -   11.0 Analysis Triggers and Use Cases    -   12.0 Extensions and Alternatives    -   13.0 Additional Disclosure

1.0 General Overview

Aspects of the present disclosure relate to computer-based systems thatare programmed for generating a long-range temperature forecast. Morespecifically, the present disclosure relates to using computer programsto generate a long-range temperature forecast or electronic digital dataprocessing apparatus for generating a long-range temperature forecast.

In addition to error introduced by the data assimilation technique usedto create the analyses/reanalyses, forecasting models are also imperfectat describing how the atmosphere evolves over time as a physicalprocess. First, physical forecasting models generally tend to haveinherent biases, such that the prediction of the environmental variablesat a future time is, on average, higher or lower than observed. Thesebiases frequently differ with the season, the time of day, the type ofweather event, and the location. Second, forecast ensembles areunderdispersive: ensemble generation techniques generally do not captureall of the uncertainty in the analysis or in the forecast model so thatforecast ensembles are not diverse enough to properly represent thepotential evolution of the real atmosphere. Techniques described hereinrelate to calibrating ensemble forecasts produced by a forecast model tocorrect for bias and underdispersion and therefore produce a betterprediction of the future state of environmental variables, especiallyfor longer ranged predictions with 14-45-day lead times. The examplesdescribed herein are primarily directed towards a forecast model thatpredicts two specific environmental variables, which are daily minimumand maximum temperature. However, the techniques described herein couldbe extended to any number of different environmental variables, such asdewpoint, relative humidity, precipitation, wind speed and direction,solar insolation, and so forth.

Temperature time series at a given location exhibit a diurnal cycle(day/night), a seasonal cycle (fall/winter/spring/summer), inter-annualvariability (for example warmer than average summers, unusually longstretches without rain, and so forth), and long-term trends (for examplefrom anthropogenic climate change), in addition to the strongvariability associated with short-term weather phenomenon.Climatological conditions are those parts of this climate signal thatrepeat year after year in a reliable way, such as the seasonal cycle.Since temperatures over the last 30 years generally show a warmingtrend, forecasts can appear skillful simply by capturing the lineartrend. In the approaches described herein, a climatology forecast isdefined to include the repeating seasonal cycle and a linear trend overthe past 30 years, and a skillful forecast is defined to be one thatoutperforms the aforementioned climatology.

In some embodiments, in order to simplify the climatology model, anassumption is made that all of the forecasting models output Gaussiantemperature distributions parameterized by the univariate mean μ and thestandard deviation σ. As a result, the overhead of dealing with largenumbers of ensemble members is avoided, which makes generating andevaluating forecasts more efficient. In addition, forecasts are made forthe average temperature for a given time period, rather than minimum andmaximum temperature, reducing the number of environmental variablesconsidered by the forecasts and eventual calibration. However, otherembodiments may assume that the forecasting models output non-Gaussiantemperature distributions and/or consider minimum and maximumtemperature separately, rather than combined as average temperature.

In an embodiment, the method of calibrating a forecast is performed asfollows. A climatology model is fit to a set of observations, such asreanalysis data which is assumed to be the base truth for theatmospheric conditions over the training period. For example, theclimatology model may include a series of harmonics representing thevarious repeating cycles (such as diurnal cycle, inter-annualvariability, and seasonal cycle) and a linear component representing thelinear trend caused by anthropogenic climate change. The fitting may bedone using any number of fitting techniques, such as usingNonhomogeneous Gaussian Regression (NGR) with the optimal model fitbeing determined by maximizing continuous ranked probability score(CRPS) by application of the Broyden-Fletcher-Goldfarb-Shanno algorithm.The climatology fitted to the observation data is referred to as theobservation climatology. The observation climatology is then used todetermine observed anomalies (observations—climatology), which representdeviations of observations from the typical temperature for a given timeand location. For example, the deviations may represent warmer or coldertemperatures than would be average for the given time and location.

The climatology model is then fitted using a set of reforecasts whichcorrespond to the times and places of the observations that weregenerated based on a forecast model that is to be calibrated. Theclimatology fit with the reforecasts is referred to as the forecastclimatology. The forecast climatology is then used to determine forecastanomalies (reforecasts—forecast climatology), which representsdeviations of the forecast model from the climatological temperature fora given time and location. One advantage to computing climatology andthereafter anomalies is that a high-resolution climatology can bedeveloped using a high-resolution observational dataset, such as PRISM,and that information can be combined with forecast skill derived from alow-resolution forecast model.

The observed anomalies are then modeled as a function of the forecastanomalies at a variety of leads. The aforementioned function is referredto as the calibrating function. For example, the calibrating functionmay be generated by applying NGR using CRPS or log-likelihood as theobjective function and a set of covariates representing features thoughtto be useful sources of forecasting skill, such as, for example, whetherrecent conditions were warmer or colder than typical and what theseasonal forecast model prediction is. Thus, given a forecast anomaly,the calibrating function returns a prediction of what the true anomalyshould have been.

In an embodiment, given a new forecast from the forecast model, theforecast climatology can be used to calculate a forecast anomaly thatcorresponds to the new forecast. The forecast anomaly can then fed asinput into the calibrating function to produce a calibrated anomalywhich is an improved prediction of the deviation from climatologicaltemperature. That calibrated anomaly is then added back to the observedclimatology to obtain a calibrated forecast. Since the calibrationmitigates the effect of biases and underdispersion inherent to thephysical forecasting model used to product the forecast, a more accurateprediction for the average temperature can be achieved compared to usingthe raw output of the physical forecasting model.

Other features and aspect of the disclosure will become apparent in thedrawings, description, and claims.

2. Example Agricultural Intelligence Computer System 2.1 StructuralOverview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant, application date, amount, source,method), (g) irrigation data (for example, application date, amount,source, method), (h) weather data (for example, precipitation,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 has one or more remote sensors 112 fixedthereon, which sensors are communicatively coupled either directly orindirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a colorgraphical screen display that is mounted within an operator's cab of theapparatus 111. Cab computer 115 may implement some or all of theoperations and functions that are described further herein for themobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object-oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 9 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 9, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline include Nitrogen,Planting, Practices, and Soil. To add a nitrogen application event, auser computer may provide input to select the nitrogen tab. The usercomputer may then select a location on the timeline for a particularfield in order to indicate an application of nitrogen on the selectedfield. In response to receiving a selection of a location on thetimeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 10, the top two timelines have the “Fall applied”program selected, which includes an application of 150 lbs. N/ac inearly April. The data manager may provide an interface for editing aprogram. In an embodiment, when a particular program is edited, eachfield that has selected the particular program is edited. For example,in FIG. 9, if the “Fall applied” program is edited to reduce theapplication of nitrogen to 130 lbs N/ac, the top two fields may beupdated with a reduced application of nitrogen based on the editedprogram.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 10, the interface may update to indicatethat the “Fall applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Fall applied” program would not alter the April application ofnitrogen.

FIG. 10 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 10, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 10. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel may include a model of past events on the one or more fields, amodel of the current status of the one or more fields, and/or a model ofpredicted events on the one or more fields. Model and field data may bestored in data structures in memory, rows in a database table, in flatfiles or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 which areprogrammed to receive, translate, and ingest field data from third partysystems via manual upload or APIs. Data types may include fieldboundaries, yield maps, as-planted maps, soil test results, as-appliedmaps, and/or management zones, among others. Data formats may includeshape files, native data formats of third parties, and/or farmmanagement information system (FMIS) exports, among others. Receivingdata may occur via manual upload, e-mail with attachment, external APIsthat push data to the mobile application, or instructions that call APIsof external systems to pull data into the mobile application. In oneembodiment, mobile computer application 200 comprises a data inbox. Inresponse to receiving a selection of the data inbox, the mobile computerapplication 200 may display a graphical user interface for manuallyuploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of application zones and/or images generatedfrom subfield soil data, such as data obtained from sensors, at a highspatial resolution (as fine as 10 meters or smaller because of theirproximity to the soil); upload of existing grower-defined zones;providing an application graph and/or a map to enable tuningapplication(s) of nitrogen across multiple zones; output of scripts todrive machinery; tools for mass data entry and adjustment; and/or mapsfor data visualization, among others. “Mass data entry,” in thiscontext, may mean entering data once and then applying the same data tomultiple fields that have been defined in the system; example data mayinclude nitrogen application data that is the same for many fields ofthe same grower, but such mass data entry applies to the entry of anytype of field data into the mobile computer application 200. Forexample, nitrogen instructions 210 may be programmed to acceptdefinitions of nitrogen planting and practices programs and to acceptuser input specifying to apply those programs across multiple fields.“Nitrogen planting programs,” in this context, refers to a stored, namedset of data that associates: a name, color code or other identifier, oneor more dates of application, types of material or product for each ofthe dates and amounts, method of application or incorporation such asinjected or knifed in, and/or amounts or rates of application for eachof the dates, crop or hybrid that is the subject of the application,among others. “Nitrogen practices programs,” in this context, refers toa stored, named set of data that associates: a practices name; aprevious crop; a tillage system; a date of primarily tillage; one ormore previous tillage systems that were used; one or more indicators ofapplication type, such as manure, that were used. Nitrogen instructions210 also may be programmed to generate and cause displaying a nitrogengraph, which indicates projections of plant use of the specifiednitrogen and whether a surplus or shortfall is predicted; in someembodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. In one embodiment, a nitrogen graphcomprises a graphical display in a computer display device comprising aplurality of rows, each row associated with and identifying a field;data specifying what crop is planted in the field, the field size, thefield location, and a graphic representation of the field perimeter; ineach row, a timeline by month with graphic indicators specifying eachnitrogen application and amount at points correlated to month names; andnumeric and/or colored indicators of surplus or shortfall, in whichcolor indicates magnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium) application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, hybrid, population, SSURGO, soil tests, orelevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at machine sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 230 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

2.4 Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge at the samelocation or an estimate of nitrogen content with a soil samplemeasurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise and distorting effects within the agronomicdata including measured outliers that would bias received field datavalues. Embodiments of agronomic data preprocessing may include, but arenot limited to, removing data values commonly associated with outlierdata values, specific measured data points that are known tounnecessarily skew other data values, data smoothing techniques used toremove or reduce additive or multiplicative effects from noise, andother filtering or data derivation techniques used to provide cleardistinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Forecast Calibration Subsystem

In an embodiment, the agricultural intelligence computer system 130,among other components, includes a forecast calibration subsystem 170.The forecast calibration subsystem 170 collects information related the“ground truth” of past environmental conditions, such asanalyses/reanalyses, and predictions on past data produced by a forecastmodel, such as forecasts/reforecasts, and generates a calibrationfunction that corrects the predictions of the forecast model. Thus,given a new forecast from the forecast model, that calibration functioncan then be used to calibrate the forecast to produce a more accurateprediction.

In an embodiment, the forecast calibration subsystem 170 is implementedvia software, for example as programmed lines of codes written inlanguages such as C++, Java, Ruby, x86 assembly and so forth, viahardware, for example by application-specific integrated circuits(ASICs) field programmable gate arrays (FPGAs), general purposehardware, and so forth, or via combinations of software and hardwarecomponents.

2.5.1 Forecast Calibration Subsystem Structural Overview

FIG. 5 illustrates an example structural overview for the forecastcalibration subsystem 170 according to an embodiment. Although aparticular number of components are depicted in FIG. 5, otherembodiments may combine the functionality of components, divide out thefunctionality of components, include additional components, or removecomponents compared to the illustration of FIG. 5. Furthermore, the jobsof each of the components illustrated in FIG. 5 could be rearrangedbetween the components. The components of FIG. 5 are referred to as“logic” components which may be implemented via software (for example asprogrammed lines of codes written in languages such as C++, Java, Ruby,x86 assembly, and so forth), via hardware (for example byapplication-specific integrated circuits (ASICs) field programmable gatearrays (FPGAs), general purpose hardware, and so forth), or viacombinations of software and hardware components.

In FIG. 5, in an embodiment, the observed climatology training logic 500represents logic that receives observation data and fits a climatologymodel to the observational data. For example, the observed climatologytraining logic 500 may be configured to retrieve reanalysis data fromthe model data and field data repository 160 and/or external data 110that represents the “ground truth” state of the atmosphere at particularplaces and times. The observed climatology training logic 500 then usesthe observation data to train/fit the climatology model. In the examplespresented herein, the environmental variable considered is temperature,thus the observation data is assumed to represent temperatures for giventimes and locations, but other environmental variables may be consideredin other embodiments. The climatology model itself is discussed in moredetail below in Section 4.0. Once the climatology model has been fit tothe observations, the result is a set of coefficients for theclimatology model that can be used to, given a location/time, produce aclimatological temperature for that location/time. The climatology modelfit by the observed climatology training logic 500 is referred to as theobserved climatology.

In an embodiment, the forecast climatology training logic 501 representslogic that receives reforecast data and fits a climatology model to thereforecast data. For example, the observed climatology training logic500 may be configured to retrieve reforecast data from the model dataand field data repository 160 and/or external data 110 that representsthe predictions of a past state of the atmosphere at a particular timeand place as determined by a forecast model. The forecast model thatproduced the reforecasts is the model whose predictions are ultimatelycalibrated by the techniques discussed herein. The forecast climatologytraining logic 501 then uses the reforecast data to train/fit theclimatology model. Once the climatology model has been fit to thereforecasts, the result is a set of coefficients for the climatologymodel that can be used to, given a location/time, produce aclimatological temperature for that location/time. The climatology modelfit by the forecast climatology training logic 501 is referred to as theforecast climatology.

In an embodiment, the calibration training logic 502 represents logicthat analyzes the observations and the reforecast data to generate acalibration function. In some embodiments, the calibration traininglogic 502 generates the calibration function by first computing observedanomalies (observations—observation climatology) and forecast anomalies(reforecasts—forecast climatology). The calibration training logic 502then generates a calibration function that models the observed anomaliesas a function of the forecast anomalies. For example, the calibrationtraining logic 502 may use a Nonhomogeneous Gaussian Regression (NGR)technique as discussed in more detail below in Sections 5.0 and 6.0 togenerate the calibration function. The generation of the calibrationfunction represents the discovery of coefficients for various covariatesconsidered by the regression. The covariates represent features of theobservations and the forecast that are considered significant sources ofpredictability and/or important for determining cases where thepredictions of the forecast model might be inaccurate.

In some embodiments, different calibration functions are produced fordifferent leads. For example, each reforecast may include thetemperature and variance for a specified location and time, as well asthe lead time which represents how far the prediction is into the futurefrom the initial state used by the forecast model. The calibrationtraining logic 502 then generates different calibration functions fordifferent lead times, for example, one for predictions with a lead timeof 15 days, one for predictions with a lead time of 20 days, and soforth. Generating different calibration functions for different leadtimes is not strictly required but may be helpful in some circumstancessince different covariates may be preferred at different lead times. Forexample, the extent to which recent conditions were warmer or colderthan typical (recent anomalies) may be a useful source of predictabilityfor the first one to two weeks of forecasts but may not be useful forforecasts at leads of three or four weeks. In this case, recentanomalies can be included as a covariate for modeling forecasts at leadsof one to two weeks and not at leads of three to four weeks. However,for simplicity, the different calibration functions can be representedas a single calibration function that takes lead time as one of itsinputs.

In an embodiment, the forecast calibration logic 503 represents logicthat takes as input a new forecast and then uses the calibrationfunction to calibrate the forecast. For example, the forecastcalibration logic 503 may take the new forecast and use the forecastclimatology to produce the climatological temperature for that locationand time. The climatological temperature can then be subtracted from thenew forecast to produce a forecast anomaly. The forecast anomaly is thenfed as input to the calibration function to produce a calibratedanomaly, which in turn is added back to the climatological temperatureto produce a calibrated forecast. Since the calibration will reduce theeffect of the inherent biases and underdispersion of the forecast modeland relies on other sources of predictability such as recent anomalies,the calibrated forecast will on average be more accurate than the rawforecast produced by the forecast model.

The term “new forecast” does not necessarily mean that the forecast isfor a time into the future. In some cases, to test the effectiveness ofthe calibration, the “new forecast” may in fact be a reforecast for atime period that is not considered when training the calibrationfunction. For example, the observations and the reforecasts may bedivided into a training set and a testing set, as is common incross-validation, where the calibration function is trained using thetraining set and the testing set is then used to verify the accuracy ofthe calibration. However, outside of testing, most cases will involvethe receipt of a forecast into the future for which no observation isavailable and the calibration function is then used to calibrate theforecast to improve its accuracy.

2.5.2 Forecast Calibration Subsystem Functional Overview

FIG. 6 illustrates an example functional overview for the forecastcalibration subsystem 170 according to an embodiment. Although theprocess flow illustrated in FIG. 6 depicts particular inputs and/oroutputs among the components of FIG. 5, those inputs and outputs are notexclusive. In particular, to avoid obscuring the figure, inputs tocomponents that are earlier in the chain are not explicitly shown asbeing available as input to components lower in the chain. However, insome embodiments, those inputs are in fact available to the latercomponents. For example, the reanalysis data 600 and reforecast data 601may be made available for use by the calibration training logic 502.Furthermore, the forecast climatology 603 may be available to theforecast calibration logic 503.

In FIG. 6, reanalysis data 600 is fed as input into the observedclimatology training logic 500. The observed climatology training logic500 then fits a climatology model to the reanalysis data 600 to produceobserved climatology 602. Similarly, reforecast data 601 is fed as inputinto the forecast climatology training logic 501. The forecastclimatology training logic 501 then fits the climatology model to thereforecast data 601 to produce a forecast climatology 603. The observedclimatology 602 and the forecast climatology 603 are then used as inputto the calibration training logic 502. The calibration training logic502 calculates deviations from the climatology (“anomalies”) for boththe reanalysis data 600 and the reforecast data 601 and generates acalibration function 604 that models the observed anomalies as afunction of the forecast anomalies. The calibration function 604 is thenused by the forecast calibration logic 503 to calibrate a new forecast605 resulting in calibrated forecast 606.

2.6 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general-purpose microprocessor.

Computer system 400 also includes a main memory 406, such as arandom-access memory (RAM) or other dynamic storage device, coupled tobus 402 for storing information and instructions to be executed byprocessor 404. Main memory 406 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 404. Such instructions, whenstored in non-transitory storage media accessible to processor 404,render computer system 400 into a special-purpose machine that iscustomized to perform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (for example, x)and a second axis (for example, y), that allows the device to specifypositions in a plane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3.0 Example System Inputs

In an embodiment, reanalysis data 600 represents a set of one or morereanalyses stored by the model data and field data repository 160 and/orexternal data 100 that is accessible to the forecast calibrationsubsystem 170. As discussed above, reanalyses are analyses that havebeen performed using a consistent data assimilation technique and/orforecasting model. For the purpose of training the calibration function604, the reanalysis data 600 is assumed to represent the “ground truth”for what the temperature actually was for a given location and time.Thus, each entry of the reanalysis data 600 may include fields such astemperature, location (for example grid location or other coordinates),time, uncertainty (for example expressed in standard deviations assuminga Gaussian distribution), and so forth. The aforementioned fields arenot exclusive since the examples provided herein calibrate predictionsfor temperature, but other environmental variables, such as moisture,barometric pressure, wind current velocities, and so forth could also becalibrated using the same calibration methodology. Unlikeforecasts/reforecasts, reanalyses do not include lead times since theyrepresent snapshots of the atmosphere at a given time and place, ratherthan a prediction for a future time. Although data pertaining toreanalysis is generally preferred for ground truth due to removinginconsistencies in the forecasting models and/or data assimilationtechniques utilized to produce the analysis, raw analysis data may beused in some embodiments as the ground truth. However, in otherembodiments reanalysis data 600 may be augmented or replaced withobservations that are not derived from an analysis/reanalysis, butrather alternative datasets that reconstruct past conditions such asPRISM. Thus, reanalysis data 600 is merely an example of observations ofwhich any type could be used for the same purposes described herein inrelation to the reanalysis data 600.

In an embodiment, reforecast data 601 represents a set of one or morereforecasts stored by the model data and field data repository 160and/or external data 100 that is accessible to the forecast calibrationsubsystem 170. As discussed above, reforecasts are forecasts generatedby a forecast model that takes into account data only up to some pointin the past and then forecasts ahead from that past point. Thus, thereforecast data 601 generally represents predictions for time periodswhen the ground truth is already available. Each entry of the reforecastdata 601 may include fields such as temperature, location (for examplegrid location or other coordinates), time, uncertainty (for exampleexpressed in standard deviations assuming a Gaussian distribution), leadtime and so forth. Furthermore, the reforecast data 601 may include, foreach location and time, a plurality of members that are collectivelyreferred to as an ensemble. Each member of the ensemble is a forecastfor the specified location and time assuming a different initial state,such as using different reanalysis to begin the simulation of thephysical process or using the same reanalysis but using perturbations toits values based on the standard deviation of the environmentalvariables in the reanalysis.

In an embodiment, new forecast 605, like reforecast data 601, representsa prediction made by the underlying forecast model, but is for a futuretime for which no observation data (such as an analysis or reanalysis)is available. Thus, reforecast data 601 and reanalysis data 600 are usedto develop a calibration function 604 based on discrepancies between theobserved and predicted temperatures. The calibration function is thenused to calibrate the new forecast 605 to improve the accuracy of thepredictions. Furthermore, like reforecasts, the new forecast 605 mayalso include multiple ensemble members.

Reanalyses, reforecasts, and forecasts are produced by a number ofweather monitoring organizations, such as the NCEP, the ECMWF, as wellas others, and are readily available for download from their respectivedatabases. Thus, in some embodiments, the reanalysis data 600,reforecast data 601, and new forecast 605 may be obtained by theagricultural intelligence computer system 130 from external data 110representing the databases maintained by the aforementioned weathermonitoring organizations. In other embodiments, the agriculturalintelligence computer system 130 may be configured to generate its ownreanalyses to store in the model data and field data repository 160 orperiodically download the latest data from the databases belonging tothe aforementioned weather monitoring organizations into the model dataand field data repository 160.

The different datasets produced by the weather monitoring organizationsmay be generated for different geographic granularities (for exampledifferent grid sizes), be produced at different temporal granularities(for example time steps of different amounts of time, such as producingan analysis/reanalysis every 6 hours, while others produce a reanalysisin time steps of a day), and so forth. Thus, for example, if thereanalysis data from one organization is used as the ground truth data,but the reforecast data is from a different organization that useddifferent geological and/or temporal granularities, smoothing and/orsubsampling may be performed so that the resolutions of the observationsand predictions match to simplify the calibration process. Further, somedatasets may include low and high temperature, rather than averagetemperature which is the environmental variable analyzed in the examplescontained herein. However, the high and low temperature in thosedatasets can be averaged together to approximate the average temperaturefor the corresponding location and time.

4.0 Climatology

In an embodiment, the univariate mean, μ(t), and standard deviation,σ(t), at each physical location are modeled as functions of time, t, andare used to define a climatology, which includes the repeating seasonalcycle and linear climate trends. Thus, the climatology model includes ascovariates a combination of a linear trend ct+d and a series of k=3harmonics (Equation 1.0):

${\mu(t)} = {{\sum\limits_{i = 1}^{k}\left( {{a_{i}\;{\sin\left( \frac{it}{T} \right)}} + {b_{i}\;{\cos\left( \frac{it}{T} \right)}}} \right)} + {ct} + d}$where μ(t) is the climatological mean for a given location and time t,variables a, b, c, and d are fit parameters, and T represents the timeperiod for cyclic components (for example a year). The climatologicalvariance σ_(t) ² is modeled without trending and using only one harmonic(Equation 2.0):

${\sigma(t)} = {{e\;{\sin\left( \frac{t}{T} \right)}} + {f\;{\cos\left( \frac{t}{T} \right)}} + g}$where e, f, and g are fit parameters.

To generate statistical ensembles at each location and time from theclimatology model, the temperatures of each day T_(i) are drawn from theequivalent climatological distribution:

T_(i)˜N(μ(t), σ(t)²) where N indicates a Gaussian distribution.

5.0 Nonhomogenous Gaussian Regression

Nonhomogeneous Gaussian Regression (NGR), also known as Ensemble ModelOutput Statistics (EMOS), is a method of post processing ensembleforecasts with the goals of removing bias and improving the ensembledispersion. NGR is a simple extension of the generalized linear modelused for climatology in which the resulting forecast distribution Y_(t)is allowed to be a function of the ensemble forecasts F_(t):Y _(t) ˜N(μ(t,F _(t)),σ(t,F _(t))²)μ(t, F_(t)) and σ(t, F_(t)) may be defined as linear functions of theensemble mean and sample standard deviation, s(F_(t)),μ(t)=α₀+α₁ F _(t)σ(t)=β₀+β₀ s(F _(t))but the aforementioned definition can be extended to include multiplepredictors/covariates X_(μ,t) and X_(σ, t), such that the resultingcalibrated distribution at time t is,Y _(t) ˜N(α^(T) X _(u,t),(β^(T) X _(σ,t))²)

In an embodiment, the climatology model described above (0127) uses thefollowing covariates:

$X_{u,t} = {{\begin{bmatrix}1 \\t \\{\sin\;\left( \frac{t}{T} \right)} \\\vdots \\{\cos\left( {\frac{3t}{T}} \right)}\end{bmatrix}\mspace{14mu}{and}\mspace{14mu} X_{\sigma.t}} = {\begin{bmatrix}1 \\{\sin\left( {\frac{t}{T}} \right)} \\{\cos\left( {\frac{t}{T}} \right)}\end{bmatrix}.}}$There are many different sets of covariates that may be used for theunivariate mean and standard deviation. Other sets of potentialcovariates that could be used for X_(u,t) and X_(σ,t) are describedbelow in Section 8.0.

The goal of the calibration training logic 502 is to find optimal valuesfor the covariate weights, α and β. There are multiple potentialobjective functions that could be used to determine the covariateweights, such as minimizing the continuous ranked probability score(CRPS) or maximizing the log-likelihood. However, to provide a concreteexample, an embodiment that utilizes CRPS is described.

Given a set of observations Z, reforecasts F, and parameters α and β,the CRPS can be computed as follows (Equation 3.0),

${{CRPS}\left( Z_{t} \right)} = {{\sigma_{t}\left( {{\frac{Z_{t} - \mu_{t}}{\sigma_{t}}\left( {{2{\Phi\left( \frac{Z_{t} - \mu_{t}}{\sigma_{t}} \right)}} - 1} \right)} + {2{\phi\left( \frac{Z_{t} - \mu_{t}}{\sigma_{t}} \right)}} - \frac{1}{\pi}} \right)}.}$where μ_(t)=α^(T)X_(μ,t) and σ_(t)=β^(T) X_(σ,t). Finding the optimalweights that minimizes the average CRPS of the calibrated forecasts isefficient since the gradient can be computed relatively quickly by aconventional Central Processing Unit (CPU):

$\frac{\partial{CRPS}}{\partial\mu_{t}} = {1 - {2{\Phi\left( \frac{Z_{t} - \mu_{t}}{\sigma_{t}} \right)}}}$$\frac{\partial{CRPS}}{\partial\sigma_{t}} = {{2{\phi\left( \frac{Z_{t} - \mu_{t}}{\sigma_{t}} \right)}} - {\frac{1}{\pi}.}}$

For example, the optimization may be performed using theBroyden-Fletcher-Goldfarb-Shanno algorithm, which uses an empiricalapproximation of the Hessian that is refined iteratively.

6.0 Modified Nonhomogenous Gaussian Regression

In an embodiment, the NGR methodology described above in Section 5.0 ismodified to calibrate anomalies relative to climatology, rather thancalibrating temperatures directly. For example, the aforementionedapproach allows a high-resolution climatology model to be built usinghigh-resolution gridded reconstruction of past conditions (such as PRISMdata) and combine that with forecast skill obtained from alow-resolution forecast model. First the observed temperature anomaliesare computed (Equation 4.0):

${A_{t} = \frac{T_{t} - \mu_{t}}{\sigma_{t}}},$where μ_(t) and σ_(t) are the mean and standard deviation of theobserved climatology 602 at time t, as described above in Section 4.0,and T_(t) represents the observed temperature at time t, such asindicated by the reanalysis data 600. In addition, the climatology isfit to the reforecasts and used to determine forecast anomalies(Equation 5.0),

${FA}_{t} = \frac{F_{t} - \mu_{t}^{Forecast}}{\sigma_{t}^{Fforecast}}$where the values μ_(t) ^(Forecast) and σ_(t) ^(Forecast) are the meanand standard deviation of the forecast climatology 603 at time t andF_(t) is the temperature at time t as indicated by the forecast, such asindicated by the reforecast data 601.

In an embodiment, NGR, such as described above in Section 5.0, is thenused to model the calibrated forecast anomalies C_(t),C ₁ ˜N(μ(t,FA _(t)),σ(t,FA _(t))²).

The calibrated forecast anomalies can then be added back to the observedclimatology 603 to arrive at the calibrated forecast temperaturedistribution (Equation 6.0),Y ₁=σ₁ C ₁+μ₁.Using this reformulation, the intuitive property is gained that whenμ(t, FA_(t))=0 and σ(t,FA_(t))=1 the calibrated forecasts exactly equalthe forecast climatology 603. Positive values in α indicate that thecorresponding covariate is positively correlated with warmer than normaltemperatures, as well as the converse.

7.0 Covariates

This section describes various sets of covariates of μ and σ which canbe used for the covariate matrices X_(μ,t) and X_(σ,t) described abovein Section 5.0. A forecast F_(t) for some t may consist of severalensemble members. Ensemble member i is noted with an additionalsubscript F_(t,i) and the forecast lead time in days is indicated withan l such that F_(t,i,l) is forecast member i valid at t that is l daysold.

The ensemble mean and ensemble standard deviation are written as:

$\begin{matrix}{{EnsembleMean}_{t,l} = {\frac{1}{k}{\sum\limits_{i}^{k}F_{t,i,l}}}} \\{= \overset{\_}{F_{t,\bullet,l}}}\end{matrix}$ $\begin{matrix}{{EnsembleSD}_{t,l} = {\frac{1}{k}{\sum\limits_{i}^{k}\left( {F_{t,i,l} - \overset{\_}{F_{t,\bullet,l}}} \right)^{2}}}} \\{= {{s\left( F_{t,\bullet,l} \right)}.}}\end{matrix}$For sub-seasonal and longer lead times (for example in the 15-45 range),it is not expected for the model to have any skill in forecastingspecific weather events. Instead, the purpose of the model is to provideinformation about longer-term shifts from climatology, such as prolongedwarming events. However, each ensemble member forecasts a specificprogression of weather events. Ideally, there would be hundreds if notthousands of ensemble members to average out the weather events andallow for subtle deviations from climatology to be identified. However,not all data sets have a large enough ensemble sets or true ensemblemembers that could be used for this purpose. Thus, in some embodiments,two additional covariates based on augmented and smoothed ensembles canbe added to the model to compensate, which include forecasts fromneighboring run times and lead times.

Augmented ensembles are produced by including forecasts from older runtimes, thus trading off forecast accuracy (which is best at the shortestpossible lead), for a reduction in weather noise. For example, aforecast for January 30^(th) with a lead of 30 days may consist offorecasts issued on January 30^(th), but could also include forecastsfrom December 27^(th), December 22^(nd), and so on. An augmented meanand standard deviation can then be computed which includes the last pleads:

$\begin{matrix}{{{AugmentedMean}(p)}_{t,l} = {\frac{1}{kp}{\sum\limits_{j}^{p}{\sum\limits_{i}^{k}F_{t,i,{l + j}}}}}} \\{= \overset{\_}{F_{t,\bullet,i,{l + p}}}}\end{matrix}$${{AugmentedSD}(p)}_{t,l} = {\frac{1}{kp}{\sum\limits_{j}^{p}{\sum\limits_{i}^{k}{\left( {F_{t,i,{l + j}} - \overset{\_}{F_{t,\bullet,i,{l + p}}}} \right)^{2}.}}}}$

As an alternative smoothing approach, smoothed ensembles incorporateforecasts from neighboring lead times instead of older run times. Forexample, a forecast for the third of January can include the ensemblesfor the second and first. The resulting covariates are referred to asthe smooth mean and standard deviation, which are derived from anensemble set which includes the last p valid times,

$\begin{matrix}{{{SmoothMean}(p)}_{t,l} = {\frac{1}{kp}{\sum\limits_{j}^{p}{\sum\limits_{i}^{k}F_{{t - j},i,l}}}}} \\{= \overset{\_}{F_{{t - p},i,\bullet,l}}}\end{matrix}$${{SmoothSD}(p)}_{t,l} = {\frac{1}{kp}{\sum\limits_{j}^{p}{\sum\limits_{i}^{k}{\left( {F_{{t - j},i,l} - \overset{\_}{F_{{t - p},i,\bullet,l}}} \right)^{2}.}}}}$

Since temperature time series have significant autocorrelation,covariates based on persistence, that recent anomalies persist in time,can also be used in some embodiments. Using the same notation, twoadditional covariates that capture the most recent anomaly as well as anaverage of the p most recent anomalies are:

RecentAnomaly_(t, l) = A_(t − l)${{SmoothAnomaly}(p)}_{t,l} = {\frac{1}{p}{\sum\limits_{i}^{p}{A_{t - l - i}.}}}$

8.0 Model Variants

From the covariates described above in Section 7.0, a multitude ofdifferent models can be constructed.

The first example model is referred to as the NGR model and consists ofthe covariants,

$X_{\mu,t} = {{\begin{bmatrix}1 \\{\sin\left( \frac{t}{T} \right)} \\{\cos\;\left( \frac{t}{T} \right)} \\{EnsembleMean} \\{{AugmentedMean}(4)} \\{{SmoothMean}(30)}\end{bmatrix}\mspace{14mu}{and}\mspace{14mu} X_{\sigma,t}} = {\begin{bmatrix}1 \\{\sin\left( \frac{t}{T} \right)} \\{\cos\;\left( \frac{t}{T} \right)} \\{EnsembleSD} \\{{AugmentedSD}(4)} \\{{SmoothSD}(30)}\end{bmatrix}.}}$This models the mean and standard deviation as a function of the time ofthe year and the current forecasts. Seasonal harmonics are includedbecause physical models often have seasonally-dependent biases.

The second example model is referred to as the Persistence model anduses only seasonal bias and the most recent anomalies as covariants,

$X_{\mu,t} = {{\begin{bmatrix}1 \\{\sin\left( \frac{t}{T} \right)} \\{\cos\;\left( \frac{t}{T} \right)} \\{RecentAnomaly} \\{{SmoothAnomaly}(30)}\end{bmatrix}\mspace{14mu}{and}\mspace{14mu} X_{\sigma,t}} = {\begin{bmatrix}1 \\{\sin\left( \frac{t}{T} \right)} \\{\cos\;\left( \frac{t}{T} \right)}\end{bmatrix}.}}$This model has more knowledge of current climate conditions thanclimatology, but, unlike dynamically based forecasts, has no knowledgeabout the evolution of weather patterns.

A third example model is a hybrid between the NGR and Persistence modelsthat contains all the covariates:

$X_{\mu,t} = {{\begin{bmatrix}1 \\{\sin\left( \frac{t}{T} \right)} \\{\cos\;\left( \frac{t}{T} \right)} \\{EnsembleMean} \\{{AugmentedMean}(4)} \\{{SmoothMean}(30)} \\{RecentAnomaly} \\{{SmoothAnomaly}(30)}\end{bmatrix}\mspace{14mu}{and}\mspace{14mu} X_{\sigma,t}} = {\begin{bmatrix}1 \\{\sin\left( \frac{t}{T} \right)} \\{\cos\;\left( \frac{t}{T} \right)} \\{EnsembleSD} \\{{AugmentedSD}(4)} \\{{SmoothSD}(30)}\end{bmatrix}.}}$

9.0 Calibration Training Process Flow

FIG. 7 illustrates an example process flow for training a calibrationfunction for a forecast model according to an embodiment. Although FIG.7 depicts a specific number of steps that are performed in a particularorder, the exact order in which the steps are depicted is not criticaland may vary between implementations. In other embodiments, the stepsdepicted may be broken down into multiple sub-steps, combined into moregeneralized steps, include additional steps, or lack particular stepscompared to FIG. 7. In the examples below, the components of theforecast calibration subsystem 170 are assumed to perform the steps. Inaddition, the “ground truth” observations are assumed to be thereanalysis data 600 and the predictions from the forecast model used totrain the calibration are assumed to be the reforecast data 601.However, as mentioned above, reanalysis data 600 is merely an example ofobservations that can be used, any other gridded reconstruction of pastconditions could also be utilized.

In FIG. 7, at block 700 the forecast calibration subsystem 170 receivesa set of observations and a set of corresponding forecasts generated bya forecast model. For example, the set of observations may berepresented by the reanalysis data 600 which provides temperatures forparticular times and places. The set of corresponding forecasts may berepresented by the reforecast data 601 which includes ensemble members,each of which provides a prediction of the temperatures that correspondto the same times and places as the members of the reanalysis data 600.Thus, in this example, the reanalysis data 600 represents the base truthfor temperatures at a given time and place and the reforecast data 601represents the corresponding temperatures produced based on the forecastmodel. As discussed above, in some embodiments, the reforecast data 601may include multiple ensemble members for a given time and place,representing the temperature values produced by the forecast modelassuming different initial states. In an embodiment, block 700 isperformed by the observed climatology training logic 500 and theforecast climatology training logic 501 which obtain the reanalysis data600 and the reforecast data 601 respectively. For example, as describedabove in Section 3.0, the reanalysis data 600 and the reforecast data601 may be obtained from the model data and field data repository 160,external data 110, or a combination of both.

At block 701, the observed climatology training logic 500 fits aclimatology model to the observations to generate an observedclimatology 602. In an embodiment, the observed climatology traininglogic 500 uses the reanalysis data 600 to fit the climatology discussedabove with respect to Equation 1.0 and Equation 2.0 in Section 5.0 togenerate the observed climatology 602. The observed climatology 602, fora given place and time, provides a value for the average temperaturethat is expected based on the observations along with the correspondingvariance. The observed climatology 602 is then provided to thecalibration training logic 502.

At block 702, the forecast climatology training logic 501 fits theclimatology model to the reforecasts to generate a forecast climatology603. In an embodiment, the forecast climatology training logic 501 usesthe reforecast data 601 to fit the climatology discussed above withrespect to Equation 1.0 and Equation 2.0 in Section 5.0 to generate theforecast climatology 603. The forecast climatology 603, for a givenplace and time, provides a value for the average temperature expected bythe forecast model along with the corresponding variance. The forecastclimatology 603 is then provided to the calibration training logic 502.

At block 703, the calibration training logic 502 computes observedanomalies and forecast anomalies. In an embodiment, the calibrationtraining logic 502 computes observed anomalies by subtracting theobservations from the observed climatology 602 as explained above inSection 6.0 in reference to Equation 4.0 of Section 6.0. In addition,the calibration training logic 502 computes forecast anomalies bysubtracting the forecasts from the forecast climatology 603 as explainedabove in Section 6.0 in reference to Equation 5.0 of Section 6.0.

At block 704, the calibration training logic 502 generates a calibrationfunction 604 by modeling the observed anomalies as a function of theforecast anomalies. In an embodiment, the calibration training logic 502is generated by applying NGR as discussed above in Section 5.0, usingthe observed anomalies as Z and the forecast anomalies as F in Equation3.0. The result is a set of covariant weights α and β for the covariatesX_(μ,t) and X_(σ,t) such that a forecast anomaly can be fed as input tothe calibration function 604 to calibrate the forecast anomaly and itscorresponding variance. Examples of covariates that can be used forX_(μ,t) and X_(σ,t) are discussed above in Section 7.0 and Section 8.0.

10.0 Forecast Calibration Process Flow

FIG. 8 illustrates an example process flow for training a calibrationfunction for a forecast model according to an embodiment. Although FIG.8 depicts a specific number of steps that are performed in a particularorder, the exact order in which the steps are depicted is not criticaland may vary between implementations. In other embodiments, the stepsdepicted may be broken down into multiple sub-steps, combined into moregeneralized steps, include additional steps, or lack particular stepscompared to FIG. 8. The steps of FIG. 8 are assumed in the followingexamples to be performed by the forecast calibration logic 503.Furthermore, the new forecast 605 is assumed to be the forecast thatwill be calibrated by the calibration function 604.

In FIG. 8, at block 800, the forecast calibration logic 503 receives anew forecast 605 to calibrate. In some embodiments, as discussed abovein Section 3.0, the new forecast 605 is obtained from the model data andfield data repository 160, external data 110, or a combination of both.Furthermore, in some embodiments, the new forecast 605 is one member ofa larger dataset, representing forecasts for various places, times, andleads. To calibrate multiple members of the dataset, the steps of FIG. 8may be repeated for each member.

At block 801, the forecast calibration logic 503 computes a forecastanomaly based on the new forecast 605. In an embodiment, the forecastcalibration logic 503 uses the forecast climatology 603 to compute aclimatological temperature for the place and time of the new forecast605 and its corresponding variance. The climatological temperature andits variance are then subtracted from climatology as explained above inSection 6.0 in reference to Equation 5.0 to produce a forecast anomaly.

At block 802, the forecast calibration logic 503 feeds the forecastanomaly as input to the calibration function 604 to produce a calibratedanomaly. In an embodiment, the calibration function 604 produced by step704 of FIG. 7 is fed the forecast anomaly produced at block 801 toreturn a calibrated forecast anomaly and its corresponding variance.

At block 803, the forecast anomaly is added back to the climatologicaltemperature to produce a calibrated forecast 606. In an embodiment, thecalibrated forecast anomaly is added back to the climatologicaltemperature based on the observed climatology 602 to generate thecalibrated forecast 606. For example, a climatological temperature canbe produced based on the observed climatology 602 for the same date/timeof the forecast being calibrated, this climatological temperature isthen adjusted by adding the calibrated forecast anomaly.

11.0 Analysis Triggers and Use Cases

In some embodiments, the generation of the calibration function 604 andthe calibration of the new forecast 605 are performed automatically bythe agricultural intelligence computer system 130. For example, theagricultural intelligence computer system 130 may collect user input(for example through one or more user interfaces of the agriculturalintelligence computer system 130 and/or cab computer 115) identifying anobservation dataset representing the ground truth (for example byspecifying a uniform resource locater (URL) or other identifier), areforecast dataset representing past predictions produced by a forecastmodel, and a forecast dataset representing future predictions producedby the forecast model. In some embodiments, the agriculturalintelligence computer system 130, in response to receiving the userinput, invokes the forecast calibration subsystem 170 to generate thecalibration function 604 based on the observation dataset and thereforecast dataset (for example according to FIG. 7 above) and cachesthe calibration function 604 in the model data and field data repository160. After the calibration function 604 is cached, the forecastcalibration subsystem 170 uses the cached calibration function 604 toproduce a calibrated forecast dataset, such as described above inrelation to FIG. 8. The calibrated forecast dataset can then be storedin the model data and field data repository 160 for further analysis orcaused to be displayed in a user interface shown on the agriculturalintelligence computer system 130, cab computer 115, or another device.

In some embodiments, the agricultural intelligence computer system 130is configured to periodically pull data from the data source of theforecast dataset and automatically calibrates any newly discoveredforecasts using the cached calibration function 604 and adds the resultto the calibrated forecast dataset. However, in some embodiments, theagricultural intelligence computer system 130 may periodicallyregenerate the calibration function 604 based on updates to thereforecast dataset that may be made available by the data source. Forexample, the calibration function 604 may be retrained each time a newupdate is discovered, once every set period of time, in response to userinput specifying to retrain the calibration function 604, and so forth.

The calibrated forecast dataset can be used to enhance the predictionsfor weather, such as identifying periods of hotter or colder thanaverage temperatures. However, there are also other applications thatcan be built up from the calibrated forecasts. For example, WeatherIndex Insurance (WII) identifies conditions which are known to likelyresult in specific amounts of loss of crop yield. Thus, instead ofsending out an inspector to identify actual loss of crop yield, insteadthe WII pays out on the policy if any of those conditions are met basedon the predicted loss in crop yield. In other to set the policies, thecalibrated forecast dataset can be used to identify periods of hotter orcolder than average temperatures which may adversely affect crop yield.Furthermore, in addition to WII, knowing which time periods are likelyto produce abnormal weather also helps identify what types of crops afarmer should plant and at what time. For example, if the temperature isexpected to be significantly hotter than usual during a critical part ofthe growth cycle of the planted crop, the farmer may decide ahead oftime to plant a type of crop or strain that is more resistant to hottertemperatures. Furthermore, in some cases a farmer may opt to plantsooner rather than later to avoid a period of abnormal weather that ispredicted to appear over a month into the future. As another example,the techniques described herein can be applied to decision making aroundpest and disease control. If the season is predicted to be hotter orcooler than normal, a grower might elect to protect against pests anddiseases that thrive in the predicted conditions.

12.0 Extensions and Alternatives

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the disclosure, and what isintended by the applicants to be the scope of the disclosure, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

13.0 Additional Disclosure

Aspects of the subject matter described herein are set out in thefollowing numbered clauses:

1. A method comprising: an agricultural intelligence computer receivingan observation dataset that identifies one or more ground truth valuesfor an environmental variable at one or more times and a reforecastdataset that identifies one or more predicted values for theenvironmental variable produced by a forecast model that correspond tothe one or more times; the agricultural intelligence computer training aclimatology on the observation dataset to generate an observedclimatology; the agricultural intelligence computer training theclimatology on the reforecast dataset to generate a forecastclimatology; the agricultural intelligence computer identifying observedanomalies based on the observed climatology and the observation dataset;the agricultural intelligence computer identifying forecast anomaliesbased on the forecast climatology and the reforecast dataset; theagricultural intelligence computer modeling the observed anomalies as afunction of the forecast anomalies to generate a calibration function;the agricultural intelligence computer receiving a new forecast producedby the forecast model; the agricultural intelligence computercalibrating the new forecast using the calibration function.

2. The method of Clause 1, wherein the reforecast dataset representspredictions produced by the forecast model for one or more past timesand the new forecast is a prediction produced by the forecast model fora future time.

3. The method of any of Clauses 1-2, wherein the environmental variablerelates to temperature.

4. The method of Clause 3, wherein the environmental variable is averagetemperature and the climatology models' climatological averagetemperature.

5. The method of any of Clauses 1-4, wherein modeling the observedanomalies as a function of the forecast anomalies is performed usingNonhomogeneous Gaussian Regression.

6. The method of Clause 5, wherein the Nonhomogeneous GaussianRegression uses minimizing continuous ranked probability score ormaximizing log-likelihood as an objective function.

7. The method of any of Clauses 5-6, wherein the Nonhomogeneous GaussianRegression uses one or more covariants representing one or more of:seasonal bias, ensemble mean, ensemble standard deviation, recentanomalies, forecasts from previous run times, or forecasts at other leadtimes.

8. The method of any of Clauses 1-7, wherein the observed datasetincludes a value and a variance for the environmental variable at theone or more times and the reforecast dataset includes a value, a leadtime, and a variance for the environmental variable at the one or moretimes.

9. The method of any of Clauses 1-8, wherein calibrating the newforecast using the calibration function is performed by: using the newforecast as input to the forecast climatology to obtain a forecastclimatological value; computing a forecast anomaly based on the forecastclimatological value and the new forecast; using the forecast anomaly asinput to the calibration function to generate a calibrated forecastanomaly; generating an observational climatological value based on atime of prediction of the new forecast using the observed climatology;adding the forecast anomaly to the observational climatological value togenerate the calibrated forecast.

10. The method of Clause 9, wherein the calibration function takes asinput the value for the forecast anomaly, a variance for the forecastanomaly, and a lead time for the new forecast and produces a calibratedvalue for the forecast anomaly and a calibrated variance for theforecast anomaly based on the lead time.

11. One or more non-transitory computer-readable media storinginstructions that, when executed by one or more computing devices,causes performance of any one of the methods recited in Clauses 1-10.

12. A system comprising one or more computing devices comprisingcomponents, implemented at least partially by computing hardware,configured to implement the steps of any one of the methods recited inClauses 1-10.

What is claimed is:
 1. A method for providing an improvement inlong-range temperature forecasting using agricultural applications, themethod comprising: an agricultural intelligence computer receiving anobservation dataset that identifies at least a forecast climatologymodel; the agricultural intelligence computer training a climatologymodel on the observation dataset to generate an observed climatologymodel; the agricultural intelligence computer identifying observedanomalies the agricultural intelligence computer identifying forecastanomalies the agricultural intelligence computer, upon receiving a newforecast produced by the forecast climatology model, calibrating the newforecast using a calibration function by: using the new forecast asinput to the forecast climatology model to obtain a forecastclimatological value; computing a forecast anomaly based on the forecastclimatological value and the new forecast; and using the forecastanomaly as input to the calibration function to generate a calibratedforecast anomaly; generating an observational climatological value basedon a time of prediction of the new forecast using the observedclimatology model; and adding the forecast anomaly to the observationalclimatological value to generate the calibrated forecast.
 2. The methodof claim 1, wherein the climatology model includes as covariates acombination of a linear trend and a series of harmonics; wherein theforecast anomalies are further identified based on a reforecast datasetthat represents predictions produced by a forecast model for one or morepast times and the new forecast is a prediction produced by the forecastmodel for a future time.
 3. The method of claim 2, wherein theobservation dataset further identifies values for an environmentalvariable that relates to temperature.
 4. The method of claim 3, whereinthe environmental variable is average temperature and a climatologymodel climatological average temperature.
 5. The method of claim 1,wherein modeling the observed anomalies as a function of the forecastanomalies is performed using Nonhomogeneous Gaussian Regression.
 6. Themethod of claim 5, wherein the Nonhomogeneous Gaussian Regression usesminimizing continuous ranked probability score or maximizinglog-likelihood as an objective function.
 7. The method of claim 5,wherein the Nonhomogeneous Gaussian Regression uses one or morecovariants representing one or more of: seasonal bias, ensemble mean,ensemble standard deviation, recent anomalies, forecasts from previousrun times, or forecasts at other lead times.
 8. The method of claim 3,wherein the observation dataset includes a value and a variance for theenvironmental variable at one or more times and the reforecast datasetincludes a value, a lead time, and a variance for the environmentalvariable at the one or more times.
 9. The method of claim 1, furthercomprising: the agricultural intelligence computer identifying theobserved anomalies based on the climatology model and the observationdataset; the agricultural intelligence computer identifying the forecastanomalies based on at least he forecast climatology model; theagricultural intelligence computer modeling the observed anomalies as afunction of the forecast anomalies to generate the calibration functionthat is used to correct the forecast climatology model based on theobserved anomalies.
 10. The method of claim 9, wherein the calibrationfunction takes, as input, the value for the forecast anomaly, a variancefor the forecast anomaly, and a lead time for the new forecast andproduces a calibrated value for the forecast anomaly and a calibratedvariance for the forecast anomaly based on the lead time.
 11. A systemfor providing an improvement in long-range temperature forecasting usingagricultural applications, the system comprising: one or moreprocessors; one or more non-transitory computer-readable storage mediumsstoring one or more instructions which, when executed by the one or moreprocessors, cause the one or more processors to perform: receiving anobservation dataset that identifies at least a forecast climatologymodel; training a climatology model on the observation dataset togenerate an observed climatology model; identifying observed anomalies;identifying forecast anomalies; upon receiving a new forecast producedby the forecast climatology model, calibrating the new forecast using acalibration function by: using the new forecast as input to the forecastclimatology model to obtain a forecast climatological value; computing aforecast anomaly based on the forecast climatological value and the newforecast; and using the forecast anomaly as input to the calibrationfunction to generate a calibrated forecast anomaly; generating anobservational climatological value based on a time of prediction of thenew forecast using the observed climatology model; and adding theforecast anomaly to the observational climatological value to generatethe calibrated forecast.
 12. The system of claim 11, wherein theclimatology model includes as covariates a combination of a linear trendand a series of harmonics; wherein the forecast anomalies are furtheridentified based on a reforecast dataset that represents predictionsproduced by a forecast model for one or more past times and the newforecast is a prediction produced by the forecast model for a futuretime.
 13. The system of claim 12, wherein the observation datasetfurther identifies values for an environmental variable that relates totemperature.
 14. The system of claim 13, wherein the environmentalvariable is average temperature and a climatology model climatologicalaverage temperature.
 15. The system of claim 11, wherein modeling theobserved anomalies as a function of the forecast anomalies is performedusing Nonhomogeneous Gaussian Regression.
 16. The system of claim 15,wherein the Nonhomogeneous Gaussian Regression uses minimizingcontinuous ranked probability score or maximizing log-likelihood as anobjective function.
 17. The system of claim 15, wherein theNonhomogeneous Gaussian Regression uses one or more covariantsrepresenting one or more of: seasonal bias, ensemble mean, ensemblestandard deviation, recent anomalies, forecasts from previous run times,or forecasts at other lead times.
 18. The system of claim 13, whereinthe observed dataset includes a value and a variance for theenvironmental variable at one or more times and the reforecast datasetincludes a value, a lead time, and a variance for the environmentalvariable at the one or more times.
 19. The system of claim 11, storingadditional instructions for: identifying the observed anomalies based onthe climatology model and the observation dataset; identifying theforecast anomalies based on at least he forecast climatology model;modeling the observed anomalies as a function of the forecast anomaliesto generate the calibration function that is used to correct theforecast climatology model based on the observed anomalies.
 20. Thesystem of claim 19, wherein the calibration function takes as input thevalue for the forecast anomaly, a variance for the forecast anomaly, anda lead time for the new forecast and produces a calibrated value for theforecast anomaly and a calibrated variance for the forecast anomalybased on the lead time.